A Human-Centered Review of Algorithms in Decision-Making in Higher Education

TL;DR

This review analyzes algorithms used in higher education decision-making, highlighting trends towards deep learning and increased data use, while emphasizing the need for human-centered design to address ethical challenges.

Contribution

It provides a systematic categorization of algorithms in higher education and critically examines the lack of human-centered approaches in their development.

Findings

Models are increasingly using deep learning techniques.

There is a rise in the use of personal and protected data.

Current algorithms often lack interpretability and human-centered design.

Abstract

The use of algorithms for decision-making in higher education is steadily growing, promising cost-savings to institutions and personalized service for students but also raising ethical challenges around surveillance, fairness, and interpretation of data. To address the lack of systematic understanding of how these algorithms are currently designed, we reviewed an extensive corpus of papers proposing algorithms for decision-making in higher education. We categorized them based on input data, computational method, and target outcome, and then investigated the interrelations of these factors with the application of human-centered lenses: theoretical, participatory, or speculative design. We found that the models are trending towards deep learning, and increased use of student personal data and protected attributes, with the target scope expanding towards automated decisions. However, despite the associated decrease in interpretability and explainability, current development predominantly fails to incorporate human-centered lenses. We discuss the challenges with these trends and advocate for a human-centered approach.